Direct Infusion Based Metabolomics Identifies Metabolic Disease in Patients’ Dried Blood Spots and Plasma
by
Hanneke A. Haijes 1,2,*, Marcel Willemsen 1, Maria Van der Ham 1, Johan Gerrits 1, Mia L. Pras-Raves 1,3
, Hubertus C. M. T. Prinsen 1, Peter M. Van Hasselt 2, Monique G. M. De Sain-van der Velden 1, Nanda M. Verhoeven-Duif 1,† and Judith J. M. Jans 1,*,†
Hanneke A. Haijes 1,2,*, Marcel Willemsen 1, Maria Van der Ham 1, Johan Gerrits 1, Mia L. Pras-Raves 1,3, Hubertus C. M. T. Prinsen 1, Peter M. Van Hasselt 2, Monique G. M. De Sain-van der Velden 1, Nanda M. Verhoeven-Duif 1,† and Judith J. M. Jans 1,*,†
1
Section Metabolic Diagnostics, Department of Genetics, University Medical Centre Utrecht, Utrecht University, Lundlaan 6, 3584 EA Utrecht, The Netherlands
2
Section Metabolic Diseases, Department of Child Health, Wilhelmina Children’s Hospital, University Medical Centre Utrecht, Utrecht University, Lundlaan 6, 3584 EA Utrecht, The Netherlands
3
Department of Clinical Epidemiology and Biostatistics, Amsterdam University Medical Centre, Meibergdreef 9, 1105 AZ Amsterdam, The Netherlands
*
Authors to whom correspondence should be addressed.
†
These authors contributed equally to this article.
Metabolites 2019, 9(1), 12; https://doi.org/10.3390/metabo9010012
Received: 19 December 2018 / Revised: 4 January 2019 / Accepted: 8 January 2019 / Published: 11 January 2019
In metabolic diagnostics, there is an emerging need for a comprehensive test to acquire a complete view of metabolite status. Here, we describe a non-quantitative direct-infusion high-resolution mass spectrometry (DI-HRMS) based metabolomics method and evaluate the method for both dried blood spots (DBS) and plasma. 110 DBS of 42 patients harboring 23 different inborn errors of metabolism (IEM) and 86 plasma samples of 38 patients harboring 21 different IEM were analyzed using DI-HRMS. A peak calling pipeline developed in R programming language provided Z-scores for ~1875 mass peaks corresponding to ~3835 metabolite annotations (including isomers) per sample. Based on metabolite Z-scores, patients were assigned a ‘most probable diagnosis’ by an investigator blinded for the known diagnoses of the patients. Based on DBS sample analysis, 37/42 of the patients, corresponding to 22/23 IEM, could be correctly assigned a ‘most probable diagnosis’. Plasma sample analysis, resulted in a correct ‘most probable diagnosis’ in 32/38 of the patients, corresponding to 19/21 IEM. The added clinical value of the method was illustrated by a case wherein DI-HRMS metabolomics aided interpretation of a variant of unknown significance (VUS) identified by whole-exome sequencing. In summary, non-quantitative DI-HRMS metabolomics in DBS and plasma is a very consistent, high-throughput and nonselective method for investigating the metabolome in genetic disease.
View Full-Text
▼
Show Figures
Graphical abstract
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited
MDPI and ACS Style
Haijes, H.A.; Willemsen, M.; Van der Ham, M.; Gerrits, J.; Pras-Raves, M.L.; Prinsen, H.C.M.T.; Van Hasselt, P.M.; De Sain-van der Velden, M.G.M.; Verhoeven-Duif, N.M.; Jans, J.J.M. Direct Infusion Based Metabolomics Identifies Metabolic Disease in Patients’ Dried Blood Spots and Plasma. Metabolites 2019, 9, 12.
Show more citation formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.
Search more from Scilit
